A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management.

IoT LoRaWAN edge computing fire detection smart bin smart city smart drop-off container smart waste management

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
07 Apr 2021
Historique:
received: 09 03 2021
revised: 26 03 2021
accepted: 02 04 2021
entrez: 30 4 2021
pubmed: 1 5 2021
medline: 1 5 2021
Statut: epublish

Résumé

Long Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3.

Identifiants

pubmed: 33917255
pii: s21082600
doi: 10.3390/s21082600
pmc: PMC8068086
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

Sensors (Basel). 2018 May 08;18(5):
pubmed: 29738472
Sensors (Basel). 2020 Feb 12;20(4):
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Sensors (Basel). 2018 Aug 29;18(9):
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Sensors (Basel). 2020 Apr 22;20(8):
pubmed: 32331464
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Auteurs

David Baldo (D)

Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.

Alessandro Mecocci (A)

Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.

Stefano Parrino (S)

Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.

Giacomo Peruzzi (G)

Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.

Alessandro Pozzebon (A)

Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.

Classifications MeSH